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A predictive model of the freight rate of the international market in Capesize dry bulk carriers

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  • Ching-Chih Chang
  • Chin-Yuan Hsieh
  • Yung-Chih Lin

Abstract

This study examines the considerable fluctuations of the world's dry bulk shipping market from November 1995 to September 2008. The major objective is to provide a forecasting model for the freight rate in relation to the second-hand ship price. The results indicate an acceptable level of prediction according to Mean Absolute Percentage Error (MAPE), with a value no more than 20%. It is anticipated that this research will prove germane to major stakeholders, including owners, charters, investors and bankers, by forecasting the freight rate and thereby expediting the decision-making process.

Suggested Citation

  • Ching-Chih Chang & Chin-Yuan Hsieh & Yung-Chih Lin, 2012. "A predictive model of the freight rate of the international market in Capesize dry bulk carriers," Applied Economics Letters, Taylor & Francis Journals, vol. 19(4), pages 313-317, March.
    • Handle: RePEc:taf:apeclt:v:19:y:2012:i:4:p:313-317
    DOI: 10.1080/13504851.2011.576998
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    1. Ghysels, Eric & Granger, Clive W J & Siklos, Pierre L, 1996. "Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Process?," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 374-386, July.
    2. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 169-177, April.
    3. Ghysels, Eric & Granger, Clive W J & Siklos, Pierre L, 1996. "Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Process? Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 396-397, July.
    4. Kavussanos, Manolis G. & Alizadeh-M, Amir H., 2001. "Seasonality patterns in dry bulk shipping spot and time charter freight rates," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 37(6), pages 443-467, December.
    5. Pakko, Michael R, 2000. "The Cyclical Relationship between Output and Prices: An Analysis in the Frequency Domain," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 32(3), pages 382-399, August.
    6. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-152, April.
    7. Jeff Tayman & David Swanson, 1996. "On the utility of population forecasts," Demography, Springer;Population Association of America (PAA), vol. 33(4), pages 523-528, November.
    8. den Butter, F A G & Mourik, T J, 1990. "Seasonal Adjustment Using Structural Time Series Models: An Application and Comparison with the Census X-11 Method," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(4), pages 385-394, October.
    9. J. Z. Easaw & S. M. Heravi & J. C. K. Ash & D. J. Smyth, 2002. "Are Hodrick-Prescott `forecasts' rational?," Empirical Economics, Springer, vol. 27(4), pages 631-643.
    10. Casals J. & Jerez M. & Sotoca S., 2002. "An Exact Multivariate Model-Based Structural Decomposition," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 553-564, June.
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    1. Wetzstein, Brian & Florax, Raymond & Foster, Kenneth & Binkley, James, 2021. "Transportation costs: Mississippi River barge rates," Journal of Commodity Markets, Elsevier, vol. 21(C).

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